Airborne laser scanning point clouds filtering method based on the construction of virtual ground seed points

Xiaoqiang Liu; Yanming Chen; Liang Cheng; Mengru Yao; Shulin Deng; Manchun Li; Dong Cai

doi:10.1117/1.JRS.11.016032

24 February 2017 Airborne laser scanning point clouds filtering method based on the construction of virtual ground seed points

Xiaoqiang Liu, Yanming Chen, Liang Cheng, Mengru Yao, Shulin Deng, Manchun Li, Dong Cai

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016032 (February 2017). https://doi.org/10.1117/1.JRS.11.016032

Abstract

Filtering of airborne laser scanning (ALS) point clouds into ground and nonground points is a core postprocessing step for ALS data. A hierarchical filtering method, which has high operating efficiency and accuracy because of the combination of multiscale morphology and progressive triangulated irregular network (TIN) densification (PTD), is proposed. In the proposed method, the grid is first constructed for the ALS point clouds, and virtual seed points are set by analyzing the shape and elevation distribution of points within the grid. Then, the virtual seed points are classified as ground or nonground using the multiscale morphological method. Finally, the virtual ground seed points are utilized to generate the initial TIN, and the filter is completed by iteratively densifying the initial TIN. We used various ALS data to test the performance of the proposed method. The experimental results show that the proposed filtering method has strong applicability for a variety of landscapes and, in particular, has lower commission error than the classical PTD filtering method in urban areas.

Citation Download Citation

Xiaoqiang Liu, Yanming Chen, Liang Cheng, Mengru Yao, Shulin Deng, Manchun Li, and Dong Cai "Airborne laser scanning point clouds filtering method based on the construction of virtual ground seed points," Journal of Applied Remote Sensing 11(1), 016032 (24 February 2017). https://doi.org/10.1117/1.JRS.11.016032

Received: 2 November 2016; Accepted: 7 February 2017; Published: 24 February 2017

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